Objective

To determine whether fingolimod, an oral sphingosine-1-phosphate receptor modulator approved for treatment of multiple sclerosis (MS), generally leads to increased retinal tissue volume.

Methods

In this longitudinal observational study, we compared changes in macular volume on spectral-domain optical coherence tomography (OCT) between consecutive patients with MS who initiated fingolimod and a matched reference cohort of patients with MS never exposed to the drug. The primary reference cohort was matched based on time interval between OCT examinations. A secondary reference cohort was matched based on age and disease duration. Change in macular volume within each group was analyzed using the paired t test. Change in macular volume between groups was examined using multiple linear regression.

Results

Macular volume increased by a mean of 0.025 mm3 (95% confidence interval [CI] +0.017 to +0.033, p < 0.001) in the 30 patients with MS who initiated fingolimod over a mean follow-up time of 5 months (SD 3). Macular volume did not significantly change over a mean follow-up time of 6 months (SD 4) in a comparison group of 30 patients with MS never treated with fingolimod (mean change of -0.003 mm3, 95% CI -0.009 to +0.004, p = 0.47). Overall, 74% of eyes in the fingolimod-treated group exhibited an increase in macular volume vs. 37% of eyes in the comparison group.

Conclusion

Initiation of fingolimod in MS is associated with a modest, relatively rapid increase in macular volume.

Background

To determine whether retinal axonal loss is detectable in patients with a clinically isolated syndrome (CIS), a first clinical demyelinating attack suggestive of multiple sclerosis (MS), and examine patterns of retinal axonal loss across MS disease subtypes.

Methodology/principal findings

Spectral-domain Optical Coherence Tomography was performed in 541 patients with MS, including 45 with high-risk CIS, 403 with relapsing-remitting (RR)MS, 60 with secondary-progressive (SP)MS and 33 with primary-progressive (PP)MS, and 53 unaffected controls. Differences in retinal nerve fiber layer (RNFL) thickness and macular volume were analyzed using multiple linear regression and associations with age and disease duration were examined in a cross-sectional analysis. In eyes without a clinical history of optic neuritis (designated as "eyes without optic neuritis"), the total and temporal peripapillary RNFL was thinner in CIS patients compared to controls (temporal RNFL by -5.4 µm [95% CI -0.9 to--9.9 µm, p = 0.02] adjusting for age and sex). The total (p = 0.01) and temporal (p = 0.03) RNFL was also thinner in CIS patients with clinical disease for less than 1 year compared to controls. In eyes without optic neuritis, total and temporal RNFL thickness was nearly identical between primary and secondary progressive MS, but total macular volume was slightly lower in the primary progressive group (p<0.05).

Conclusions/significance

Retinal axonal loss is increasingly prominent in more advanced stages of disease--progressive MS>RRMS>CIS--with proportionally greater thinning in eyes previously affected by clinically evident optic neuritis. Retinal axonal loss begins early in the course of MS. In the absence of clinically evident optic neuritis, RNFL thinning is nearly identical between progressive MS subtypes.

Objective

To evaluate the inter-rater reliability of semiautomated segmentation of spectral domain optical coherence tomography (OCT) macular volume scans.

Methods

Macular OCT volume scans of left eyes from 17 subjects (8 patients with MS and 9 healthy controls) were automatically segmented by Heidelberg Eye Explorer (v1.9.3.0) beta-software (Spectralis Viewing Module v6.0.0.7), followed by manual correction by 5 experienced operators from 5 different academic centers. The mean thicknesses within a 6-mm area around the fovea were computed for the retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer (OPL), and outer nuclear layer (ONL). Intraclass correlation coefficients (ICCs) were calculated for mean layer thickness values. Spatial distribution of ICC values for the segmented volume scans was investigated using heat maps.

Results

Agreement between raters was good (ICC > 0.84) for all retinal layers, particularly inner retinal layers showed excellent agreement across raters (ICC > 0.96). Spatial distribution of ICC showed highest values in the perimacular area, whereas the ICCs were poorer for the foveola and the more peripheral macular area. The automated segmentation of the OPL and ONL required the most correction and showed the least agreement, whereas differences were less prominent for the remaining layers.

Conclusions

Automated segmentation with manual correction of macular OCT scans is highly reliable when performed by experienced raters and can thus be applied in multicenter settings. Reliability can be improved by restricting analysis to the perimacular area and compound segmentation of GCL and IPL.

Objective

To develop consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results.

Methods

A panel of experienced OCT researchers (including 11 neurologists, 2 ophthalmologists, and 2 neuroscientists) discussed requirements for performing and reporting quantitative analyses of retinal morphology and developed a list of initial recommendations based on experience and previous studies. The list of recommendations was subsequently revised during several meetings of the coordinating group.

Results

We provide a 9-point checklist encompassing aspects deemed relevant when reporting quantitative OCT studies. The areas covered are study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition data analysis, recommended nomenclature, and statistical analysis.

Conclusions

The Advised Protocol for OCT Study Terminology and Elements recommendations include core items to standardize and improve quality of reporting in quantitative OCT studies. The recommendations will make reporting of quantitative OCT studies more consistent and in line with existing standards for reporting research in other biomedical areas. The recommendations originated from expert consensus and thus represent Class IV evidence. They will need to be regularly adjusted according to new insights and practices.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.